Overview

Dataset statistics

Number of variables13
Number of observations50
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.2 KiB
Average record size in memory106.6 B

Variable types

Categorical1
Numeric12

Alerts

Binding Energy is highly correlated with Heat of ReactionHigh correlation
Activation Energy is highly correlated with Heat of Reaction and 3 other fieldsHigh correlation
Heat of Reaction is highly correlated with Binding Energy and 1 other fieldsHigh correlation
C-F Bond Length is highly correlated with LUMO of Equilibrium Structure and 4 other fieldsHigh correlation
LUMO of Equilibrium Structure is highly correlated with C-F Bond Length and 3 other fieldsHigh correlation
HOMO of Equilibrium Structure is highly correlated with Activation Energy and 4 other fieldsHigh correlation
b-SOMO is highly correlated with Activation Energy and 4 other fieldsHigh correlation
C1-Mulliken is highly correlated with F-MullikenHigh correlation
F-Mulliken is highly correlated with C-F Bond Length and 3 other fieldsHigh correlation
C1-NBO is highly correlated with F-MullikenHigh correlation
F-NBO is highly correlated with Activation Energy and 5 other fieldsHigh correlation
Activation Energy is highly correlated with Heat of Reaction and 3 other fieldsHigh correlation
Heat of Reaction is highly correlated with Activation Energy and 1 other fieldsHigh correlation
C-F Bond Length is highly correlated with LUMO of Equilibrium Structure and 4 other fieldsHigh correlation
LUMO of Equilibrium Structure is highly correlated with C-F Bond Length and 3 other fieldsHigh correlation
HOMO of Equilibrium Structure is highly correlated with Activation Energy and 4 other fieldsHigh correlation
b-SOMO is highly correlated with Activation Energy and 4 other fieldsHigh correlation
F-Mulliken is highly correlated with C-F Bond LengthHigh correlation
F-NBO is highly correlated with Activation Energy and 5 other fieldsHigh correlation
Activation Energy is highly correlated with Heat of Reaction and 1 other fieldsHigh correlation
Heat of Reaction is highly correlated with Activation EnergyHigh correlation
C-F Bond Length is highly correlated with HOMO of Equilibrium Structure and 2 other fieldsHigh correlation
LUMO of Equilibrium Structure is highly correlated with HOMO of Equilibrium Structure and 2 other fieldsHigh correlation
HOMO of Equilibrium Structure is highly correlated with Activation Energy and 4 other fieldsHigh correlation
b-SOMO is highly correlated with LUMO of Equilibrium Structure and 2 other fieldsHigh correlation
F-Mulliken is highly correlated with C-F Bond Length and 1 other fieldsHigh correlation
F-NBO is highly correlated with C-F Bond Length and 4 other fieldsHigh correlation
function groups/parameters is highly correlated with Binding Energy and 11 other fieldsHigh correlation
Binding Energy is highly correlated with function groups/parameters and 8 other fieldsHigh correlation
Activation Energy is highly correlated with function groups/parameters and 9 other fieldsHigh correlation
Heat of Reaction is highly correlated with function groups/parameters and 9 other fieldsHigh correlation
C-F Bond Length is highly correlated with function groups/parameters and 11 other fieldsHigh correlation
LUMO of Equilibrium Structure is highly correlated with function groups/parameters and 9 other fieldsHigh correlation
HOMO of Equilibrium Structure is highly correlated with function groups/parameters and 8 other fieldsHigh correlation
C-F Bond Energy is highly correlated with function groups/parameters and 7 other fieldsHigh correlation
b-SOMO is highly correlated with function groups/parameters and 9 other fieldsHigh correlation
C1-Mulliken is highly correlated with function groups/parameters and 4 other fieldsHigh correlation
F-Mulliken is highly correlated with function groups/parameters and 10 other fieldsHigh correlation
C1-NBO is highly correlated with function groups/parameters and 6 other fieldsHigh correlation
F-NBO is highly correlated with function groups/parameters and 10 other fieldsHigh correlation
function groups/parameters is uniformly distributed Uniform
function groups/parameters has unique values Unique
Binding Energy has unique values Unique
Heat of Reaction has unique values Unique
C-F Bond Energy has unique values Unique

Reproduction

Analysis started2022-04-05 07:31:06.859768
Analysis finished2022-04-05 07:31:35.015521
Duration28.16 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

function groups/parameters
Categorical

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size528.0 B
无取代
 
1
o-Me
 
1
m-NMe2
 
1
m-CH2OH
 
1
m-CH2OMe
 
1
Other values (45)
45 

Length

Max length8
Median length6
Mean length5.66
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)100.0%

Sample

1st row无取代
2nd rowp-F
3rd rowp-COH
4th rowp-COMe
5th rowp-CN

Common Values

ValueCountFrequency (%)
无取代1
 
2.0%
o-Me 1
 
2.0%
m-NMe21
 
2.0%
m-CH2OH1
 
2.0%
m-CH2OMe1
 
2.0%
m-n1
 
2.0%
o-F 1
 
2.0%
o-COH 1
 
2.0%
o-COMe1
 
2.0%
o-CN 1
 
2.0%
Other values (40)40
80.0%

Length

2022-04-05T15:31:35.139188image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
无取代1
 
2.0%
p-nh21
 
2.0%
m-ipr1
 
2.0%
p-coh1
 
2.0%
p-come1
 
2.0%
p-cn1
 
2.0%
p-cf31
 
2.0%
p-no21
 
2.0%
p-me1
 
2.0%
p-ipr1
 
2.0%
Other values (40)40
80.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Binding Energy
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-21.28118037
Minimum-23.59553062
Maximum-19.13560369
Zeros0
Zeros (%)0.0%
Negative50
Negative (%)100.0%
Memory size528.0 B
2022-04-05T15:31:35.270872image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-23.59553062
5-th percentile-23.33416549
Q1-21.87107666
median-21.11685984
Q3-20.57840606
95-th percentile-19.99177991
Maximum-19.13560369
Range4.459926925
Interquartile range (IQR)1.2926706

Descriptive statistics

Standard deviation1.039082105
Coefficient of variation (CV)-0.04882633797
Kurtosis-0.04731409737
Mean-21.28118037
Median Absolute Deviation (MAD)0.6625438833
Skewness-0.5430513097
Sum-1064.059018
Variance1.079691621
MonotonicityNot monotonic
2022-04-05T15:31:35.458335image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-20.01436871
 
2.0%
-20.43517691
 
2.0%
-23.381681491
 
2.0%
-20.807083261
 
2.0%
-20.80346881
 
2.0%
-20.933087261
 
2.0%
-20.352973091
 
2.0%
-21.770518191
 
2.0%
-19.135603691
 
2.0%
-21.89727521
 
2.0%
Other values (40)40
80.0%
ValueCountFrequency (%)
-23.595530621
2.0%
-23.393886551
2.0%
-23.381681491
2.0%
-23.276090381
2.0%
-23.206210861
2.0%
-23.012523631
2.0%
-22.7312361
2.0%
-22.409950881
2.0%
-21.952458431
2.0%
-21.950990061
2.0%
ValueCountFrequency (%)
-19.135603691
2.0%
-19.517481181
2.0%
-19.973298171
2.0%
-20.01436871
2.0%
-20.10887171
2.0%
-20.113264281
2.0%
-20.184172911
2.0%
-20.352973091
2.0%
-20.405056431
2.0%
-20.43517691
2.0%

Activation Energy
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct49
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.47222376
Minimum21.83923053
Maximum37.15235706
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-04-05T15:31:35.605972image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum21.83923053
5-th percentile27.11938205
Q130.42719748
median31.66938176
Q333.17884138
95-th percentile34.89008938
Maximum37.15235706
Range15.31312653
Interquartile range (IQR)2.751643899

Descriptive statistics

Standard deviation2.747875482
Coefficient of variation (CV)0.08731113196
Kurtosis2.438619804
Mean31.47222376
Median Absolute Deviation (MAD)1.461502166
Skewness-1.048772562
Sum1573.611188
Variance7.550819665
MonotonicityNot monotonic
2022-04-05T15:31:35.756538image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
32.564631452
 
4.0%
32.347073731
 
2.0%
31.051704841
 
2.0%
31.650136031
 
2.0%
31.341771341
 
2.0%
31.492781621
 
2.0%
30.242216941
 
2.0%
28.169551411
 
2.0%
24.71761891
 
2.0%
27.395831581
 
2.0%
Other values (39)39
78.0%
ValueCountFrequency (%)
21.839230531
2.0%
24.71761891
2.0%
26.893196071
2.0%
27.395831581
2.0%
27.901083811
2.0%
28.169551411
2.0%
28.522212031
2.0%
29.058105571
2.0%
29.184235081
2.0%
29.285264191
2.0%
ValueCountFrequency (%)
37.152357061
2.0%
35.96098541
2.0%
35.293044931
2.0%
34.397588161
2.0%
34.225305291
2.0%
34.20557011
2.0%
34.184736771
2.0%
34.097487781
2.0%
33.990334171
2.0%
33.624677821
2.0%

Heat of Reaction
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-3.500937527
Minimum-21.41377875
Maximum2.338459941
Zeros0
Zeros (%)0.0%
Negative45
Negative (%)90.0%
Memory size528.0 B
2022-04-05T15:31:35.936059image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-21.41377875
5-th percentile-8.221573269
Q1-4.117908873
median-2.28350889
Q3-1.459020363
95-th percentile0.1552029893
Maximum2.338459941
Range23.75223869
Interquartile range (IQR)2.65888851

Descriptive statistics

Standard deviation4.193380544
Coefficient of variation (CV)-1.197787882
Kurtosis10.07829134
Mean-3.500937527
Median Absolute Deviation (MAD)1.244631572
Skewness-2.818948523
Sum-175.0468763
Variance17.58444039
MonotonicityNot monotonic
2022-04-05T15:31:36.084693image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-2.257153471
 
2.0%
-2.027484811
 
2.0%
2.3384599411
 
2.0%
-1.4468184311
 
2.0%
-1.3952998611
 
2.0%
-4.0402670611
 
2.0%
-8.955195211
 
2.0%
-4.814256721
 
2.0%
-19.799823031
 
2.0%
-6.688629091
 
2.0%
Other values (40)40
80.0%
ValueCountFrequency (%)
-21.413778751
2.0%
-19.799823031
2.0%
-8.955195211
2.0%
-7.324924231
2.0%
-7.148593921
2.0%
-6.688629091
2.0%
-6.573167251
2.0%
-5.966992591
2.0%
-5.221510711
2.0%
-5.099146261
2.0%
ValueCountFrequency (%)
2.3384599411
2.0%
1.8067456171
2.0%
0.1759098781
2.0%
0.12989456991
2.0%
0.0221636531
2.0%
-0.71105040631
2.0%
-0.71936491371
2.0%
-0.98510912361
2.0%
-1.096259971
2.0%
-1.2558106631
2.0%

C-F Bond Length
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct47
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.342047
Minimum1.3264
Maximum1.35283
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-04-05T15:31:36.222328image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1.3264
5-th percentile1.33473
Q11.3394825
median1.34316
Q31.3441625
95-th percentile1.349543
Maximum1.35283
Range0.02643
Interquartile range (IQR)0.00468

Descriptive statistics

Standard deviation0.004782290993
Coefficient of variation (CV)0.003563430337
Kurtosis1.455709155
Mean1.342047
Median Absolute Deviation (MAD)0.002385
Skewness-0.6115242447
Sum67.10235
Variance2.287030714 × 10-5
MonotonicityNot monotonic
2022-04-05T15:31:36.359960image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
1.343412
 
4.0%
1.343762
 
4.0%
1.343552
 
4.0%
1.349571
 
2.0%
1.343491
 
2.0%
1.339011
 
2.0%
1.337221
 
2.0%
1.341991
 
2.0%
1.344611
 
2.0%
1.332821
 
2.0%
Other values (37)37
74.0%
ValueCountFrequency (%)
1.32641
2.0%
1.332821
2.0%
1.334551
2.0%
1.334951
2.0%
1.334971
2.0%
1.336481
2.0%
1.337061
2.0%
1.337221
2.0%
1.337651
2.0%
1.337761
2.0%
ValueCountFrequency (%)
1.352831
2.0%
1.349651
2.0%
1.349571
2.0%
1.349511
2.0%
1.348121
2.0%
1.347281
2.0%
1.34691
2.0%
1.346861
2.0%
1.345421
2.0%
1.34531
2.0%

LUMO of Equilibrium Structure
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct48
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0328868
Minimum-0.03553
Maximum0.06306
Zeros0
Zeros (%)0.0%
Negative9
Negative (%)18.0%
Memory size528.0 B
2022-04-05T15:31:36.497557image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-0.03553
5-th percentile-0.0212785
Q10.0138275
median0.04838
Q30.053715
95-th percentile0.0595835
Maximum0.06306
Range0.09859
Interquartile range (IQR)0.0398875

Descriptive statistics

Standard deviation0.02742479166
Coefficient of variation (CV)0.8339148735
Kurtosis-0.1104276788
Mean0.0328868
Median Absolute Deviation (MAD)0.00911
Skewness-1.021851985
Sum1.64434
Variance0.0007521191977
MonotonicityNot monotonic
2022-04-05T15:31:36.669101image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0.051252
 
4.0%
0.050192
 
4.0%
0.049131
 
2.0%
0.053721
 
2.0%
0.05371
 
2.0%
0.026981
 
2.0%
0.043331
 
2.0%
-0.009341
 
2.0%
0.000451
 
2.0%
0.000391
 
2.0%
Other values (38)38
76.0%
ValueCountFrequency (%)
-0.035531
2.0%
-0.030991
2.0%
-0.030491
2.0%
-0.010021
2.0%
-0.009341
2.0%
-0.002641
2.0%
-0.001291
2.0%
-0.000841
2.0%
-0.000361
2.0%
0.000391
2.0%
ValueCountFrequency (%)
0.063061
2.0%
0.060491
2.0%
0.060111
2.0%
0.058941
2.0%
0.056751
2.0%
0.055621
2.0%
0.054671
2.0%
0.05441
2.0%
0.054361
2.0%
0.054241
2.0%

HOMO of Equilibrium Structure
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct48
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.3234012
Minimum-0.36007
Maximum-0.2683
Zeros0
Zeros (%)0.0%
Negative50
Negative (%)100.0%
Memory size528.0 B
2022-04-05T15:31:36.812750image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-0.36007
5-th percentile-0.356503
Q1-0.344705
median-0.32234
Q3-0.3099375
95-th percentile-0.2832975
Maximum-0.2683
Range0.09177
Interquartile range (IQR)0.0347675

Descriptive statistics

Standard deviation0.02314947089
Coefficient of variation (CV)-0.07158127704
Kurtosis-0.4134015663
Mean-0.3234012
Median Absolute Deviation (MAD)0.0181
Skewness0.4258604206
Sum-16.17006
Variance0.0005358980026
MonotonicityNot monotonic
2022-04-05T15:31:37.074049image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
-0.318242
 
4.0%
-0.319772
 
4.0%
-0.330671
 
2.0%
-0.322531
 
2.0%
-0.322151
 
2.0%
-0.345351
 
2.0%
-0.335391
 
2.0%
-0.345081
 
2.0%
-0.341561
 
2.0%
-0.350581
 
2.0%
Other values (38)38
76.0%
ValueCountFrequency (%)
-0.360071
2.0%
-0.357481
2.0%
-0.356711
2.0%
-0.356251
2.0%
-0.351631
2.0%
-0.351311
2.0%
-0.350581
2.0%
-0.348821
2.0%
-0.348351
2.0%
-0.346631
2.0%
ValueCountFrequency (%)
-0.26831
2.0%
-0.273581
2.0%
-0.283051
2.0%
-0.28361
2.0%
-0.287961
2.0%
-0.289261
2.0%
-0.296291
2.0%
-0.298971
2.0%
-0.300231
2.0%
-0.303471
2.0%

C-F Bond Energy
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.7331188
Minimum121.9791589
Maximum131.285151
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-04-05T15:31:37.259521image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum121.9791589
5-th percentile124.5152405
Q1126.4301171
median126.8505033
Q3127.0312199
95-th percentile128.8823992
Maximum131.285151
Range9.30599214
Interquartile range (IQR)0.6011028575

Descriptive statistics

Standard deviation1.374820862
Coefficient of variation (CV)0.01084815773
Kurtosis5.606771454
Mean126.7331188
Median Absolute Deviation (MAD)0.1917106
Skewness-0.3015239137
Sum6336.655938
Variance1.890132403
MonotonicityNot monotonic
2022-04-05T15:31:37.436050image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.83257221
 
2.0%
127.21447481
 
2.0%
127.14078631
 
2.0%
127.04502831
 
2.0%
127.0341411
 
2.0%
125.97404441
 
2.0%
127.23957521
 
2.0%
125.54027811
 
2.0%
123.67657341
 
2.0%
126.11193971
 
2.0%
Other values (40)40
80.0%
ValueCountFrequency (%)
121.97915891
2.0%
123.1174621
2.0%
123.67657341
2.0%
125.54027811
2.0%
125.85089561
2.0%
125.97404441
2.0%
125.99710541
2.0%
126.11193971
2.0%
126.15915981
2.0%
126.16807051
2.0%
ValueCountFrequency (%)
131.2851511
2.0%
130.02948471
2.0%
128.9840531
2.0%
128.75815571
2.0%
127.23957521
2.0%
127.21447481
2.0%
127.14078631
2.0%
127.07014751
2.0%
127.05464171
2.0%
127.04502831
2.0%

b-SOMO
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct48
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.3223772
Minimum-0.36359
Maximum-0.26981
Zeros0
Zeros (%)0.0%
Negative50
Negative (%)100.0%
Memory size528.0 B
2022-04-05T15:31:37.612614image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-0.36359
5-th percentile-0.354135
Q1-0.336285
median-0.3252
Q3-0.30945
95-th percentile-0.2791465
Maximum-0.26981
Range0.09378
Interquartile range (IQR)0.026835

Descriptive statistics

Standard deviation0.02206887189
Coefficient of variation (CV)-0.0684566771
Kurtosis0.1388779968
Mean-0.3223772
Median Absolute Deviation (MAD)0.01331
Skewness0.5424662479
Sum-16.11886
Variance0.0004870351063
MonotonicityNot monotonic
2022-04-05T15:31:37.771153image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
-0.325572
 
4.0%
-0.326152
 
4.0%
-0.328261
 
2.0%
-0.322351
 
2.0%
-0.322681
 
2.0%
-0.328241
 
2.0%
-0.33661
 
2.0%
-0.340981
 
2.0%
-0.330521
 
2.0%
-0.35491
 
2.0%
Other values (38)38
76.0%
ValueCountFrequency (%)
-0.363591
2.0%
-0.356081
2.0%
-0.35491
2.0%
-0.35321
2.0%
-0.351931
2.0%
-0.351271
2.0%
-0.350321
2.0%
-0.346721
2.0%
-0.344661
2.0%
-0.340981
2.0%
ValueCountFrequency (%)
-0.269811
2.0%
-0.271871
2.0%
-0.273851
2.0%
-0.285621
2.0%
-0.288371
2.0%
-0.288691
2.0%
-0.298631
2.0%
-0.299681
2.0%
-0.304371
2.0%
-0.304951
2.0%

C1-Mulliken
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct47
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.35264192
Minimum0.209498
Maximum0.446028
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-04-05T15:31:37.912776image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0.209498
5-th percentile0.3092469
Q10.32318775
median0.346
Q30.3865
95-th percentile0.40575
Maximum0.446028
Range0.23653
Interquartile range (IQR)0.06331225

Descriptive statistics

Standard deviation0.03985685454
Coefficient of variation (CV)0.1130235865
Kurtosis2.099268593
Mean0.35264192
Median Absolute Deviation (MAD)0.0286325
Skewness-0.5263561256
Sum17.632096
Variance0.001588568854
MonotonicityNot monotonic
2022-04-05T15:31:38.054431image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0.3883
 
6.0%
0.3892
 
4.0%
0.3391
 
2.0%
0.3323371
 
2.0%
0.2094981
 
2.0%
0.3241
 
2.0%
0.3521
 
2.0%
0.3411
 
2.0%
0.3871
 
2.0%
0.3791
 
2.0%
Other values (37)37
74.0%
ValueCountFrequency (%)
0.2094981
2.0%
0.3081661
2.0%
0.3085081
2.0%
0.310151
2.0%
0.3156871
2.0%
0.3167351
2.0%
0.3181111
2.0%
0.3185741
2.0%
0.3189061
2.0%
0.3207221
2.0%
ValueCountFrequency (%)
0.4460281
 
2.0%
0.4171
 
2.0%
0.4081
 
2.0%
0.4031
 
2.0%
0.4027721
 
2.0%
0.3951
 
2.0%
0.391
 
2.0%
0.3892
4.0%
0.3883
6.0%
0.3871
 
2.0%

F-Mulliken
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct40
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.27952412
Minimum-0.315
Maximum-0.233227
Zeros0
Zeros (%)0.0%
Negative50
Negative (%)100.0%
Memory size528.0 B
2022-04-05T15:31:38.199043image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-0.315
5-th percentile-0.31065
Q1-0.30575
median-0.288
Q3-0.2581355
95-th percentile-0.24222735
Maximum-0.233227
Range0.081773
Interquartile range (IQR)0.0476145

Descriptive statistics

Standard deviation0.02549751219
Coefficient of variation (CV)-0.09121757433
Kurtosis-1.527206341
Mean-0.27952412
Median Absolute Deviation (MAD)0.0215485
Skewness0.1847199274
Sum-13.976206
Variance0.000650123128
MonotonicityNot monotonic
2022-04-05T15:31:38.370553image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
-0.3067
 
14.0%
-0.3052
 
4.0%
-0.2922
 
4.0%
-0.2952
 
4.0%
-0.3092
 
4.0%
-0.2447041
 
2.0%
-0.2891
 
2.0%
-0.2911
 
2.0%
-0.2711
 
2.0%
-0.281
 
2.0%
Other values (30)30
60.0%
ValueCountFrequency (%)
-0.3151
 
2.0%
-0.3141
 
2.0%
-0.3121
 
2.0%
-0.3092
 
4.0%
-0.3071
 
2.0%
-0.3067
14.0%
-0.3052
 
4.0%
-0.3031
 
2.0%
-0.3021
 
2.0%
-0.31
 
2.0%
ValueCountFrequency (%)
-0.2332271
2.0%
-0.2362031
2.0%
-0.2418841
2.0%
-0.2426471
2.0%
-0.2447041
2.0%
-0.248011
2.0%
-0.2492741
2.0%
-0.2505071
2.0%
-0.2520911
2.0%
-0.2521141
2.0%

C1-NBO
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct47
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4326192
Minimum0.366
Maximum0.63947
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-04-05T15:31:38.520187image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0.366
5-th percentile0.3796
Q10.42025
median0.431925
Q30.4516325
95-th percentile0.4645915
Maximum0.63947
Range0.27347
Interquartile range (IQR)0.0313825

Descriptive statistics

Standard deviation0.03990044196
Coefficient of variation (CV)0.09222993793
Kurtosis14.16423937
Mean0.4326192
Median Absolute Deviation (MAD)0.01684
Skewness2.643808834
Sum21.63096
Variance0.001592045269
MonotonicityNot monotonic
2022-04-05T15:31:38.657819image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0.4272
 
4.0%
0.4212
 
4.0%
0.4222
 
4.0%
0.4281
 
2.0%
0.438091
 
2.0%
0.385191
 
2.0%
0.3661
 
2.0%
0.4661
 
2.0%
0.4571
 
2.0%
0.4811
 
2.0%
Other values (37)37
74.0%
ValueCountFrequency (%)
0.3661
2.0%
0.3751
2.0%
0.3761
2.0%
0.3841
2.0%
0.385191
2.0%
0.3861
2.0%
0.3871
2.0%
0.3931
2.0%
0.3961
2.0%
0.4021
2.0%
ValueCountFrequency (%)
0.639471
2.0%
0.4811
2.0%
0.4661
2.0%
0.462871
2.0%
0.4591
2.0%
0.4581
2.0%
0.4571
2.0%
0.454911
2.0%
0.454571
2.0%
0.4541
2.0%

F-NBO
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct40
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.36571
Minimum-0.382
Maximum-0.333
Zeros0
Zeros (%)0.0%
Negative50
Negative (%)100.0%
Memory size528.0 B
2022-04-05T15:31:38.794419image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-0.382
5-th percentile-0.376
Q1-0.371
median-0.36802
Q3-0.361
95-th percentile-0.353
Maximum-0.333
Range0.049
Interquartile range (IQR)0.01

Descriptive statistics

Standard deviation0.008701550648
Coefficient of variation (CV)-0.02379358138
Kurtosis2.891285917
Mean-0.36571
Median Absolute Deviation (MAD)0.00498
Skewness1.268249994
Sum-18.2855
Variance7.571698367 × 10-5
MonotonicityNot monotonic
2022-04-05T15:31:38.930082image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
-0.3714
 
8.0%
-0.3733
 
6.0%
-0.3763
 
6.0%
-0.3612
 
4.0%
-0.372
 
4.0%
-0.3532
 
4.0%
-0.3691
 
2.0%
-0.360951
 
2.0%
-0.3621
 
2.0%
-0.3641
 
2.0%
Other values (30)30
60.0%
ValueCountFrequency (%)
-0.3821
 
2.0%
-0.3763
6.0%
-0.3751
 
2.0%
-0.3741
 
2.0%
-0.373441
 
2.0%
-0.3733
6.0%
-0.3721
 
2.0%
-0.371131
 
2.0%
-0.3714
8.0%
-0.370711
 
2.0%
ValueCountFrequency (%)
-0.3331
2.0%
-0.3471
2.0%
-0.3532
4.0%
-0.3541
2.0%
-0.355271
2.0%
-0.3561
2.0%
-0.356231
2.0%
-0.357581
2.0%
-0.361
2.0%
-0.360391
2.0%

Interactions

2022-04-05T15:31:33.049773image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:15.289242image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:18.388986image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:19.943798image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:21.473709image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:22.928853image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:24.410861image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:25.837048image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:27.124608image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:28.611633image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:29.981971image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:31.597652image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:33.159480image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:15.480724image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:18.503645image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:20.059489image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:21.576473image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:23.042549image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:24.534528image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:25.936781image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:27.238309image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:28.724330image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:30.100652image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:31.704369image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:33.285144image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:15.651268image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:18.619337image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:20.173217image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:21.688160image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:23.160203image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:24.672164image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:26.041501image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:27.356021image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:28.843015image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:30.223325image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:31.833024image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:33.382881image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:15.780924image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:18.750987image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:20.282892image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:21.786872image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:23.261964image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:24.774889image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:26.134281image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:27.456720image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:28.944742image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:30.334065image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:31.948714image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:33.482650image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:15.953462image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:18.909562image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:20.383654image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:21.904557image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:23.366651image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:24.889610image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:26.230033image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:27.701067image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:29.060433image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:30.439750image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:32.076375image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:33.588332image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:16.113034image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:19.032235image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:20.506294image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:22.044185image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:23.478351image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:24.999320image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:26.329733image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:27.807816image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:29.195074image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:30.576385image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:32.203038image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:33.706017image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:17.626028image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:19.145931image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:20.650909image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:22.162869image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:23.600027image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:25.110987image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:26.425507image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:27.913498image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:29.307805image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:30.699054image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:32.357658image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:33.811768image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:17.743713image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:19.274587image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:20.759617image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:22.301498image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:23.731673image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:25.222692image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:26.528200image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:28.029190image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:29.413524image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:30.935458image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:32.471317image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:33.917453image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:17.900260image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:19.427180image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:20.872348image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:22.404222image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:23.836393image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:25.338380image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:26.663837image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:28.146907image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:29.516215image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:31.059093image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:32.583019image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:34.022209image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:18.014987image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:19.541905image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:21.116696image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:22.520947image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:23.955111image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:25.467065image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:26.773545image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:28.264561image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:29.617943image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:31.170793image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:32.715665image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:34.280483image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:18.136654image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:19.669564image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:21.254295image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:22.639628image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:24.075791image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:25.609656image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:26.904198image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:28.384242image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:29.741613image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:31.333360image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:32.840364image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:34.393182image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:18.273264image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:19.789213image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:21.376017image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:22.766256image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:24.179512image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:25.732357image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:27.016930image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:28.485967image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:29.869305image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:31.467999image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2022-04-05T15:31:32.947048image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Correlations

2022-04-05T15:31:39.062703image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-04-05T15:31:39.325002image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-04-05T15:31:39.692020image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-04-05T15:31:39.910436image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-04-05T15:31:34.619578image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-04-05T15:31:34.893876image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

function groups/parametersBinding EnergyActivation EnergyHeat of ReactionC-F Bond LengthLUMO of Equilibrium StructureHOMO of Equilibrium StructureC-F Bond Energyb-SOMOC1-MullikenF-MullikenC1-NBOF-NBO
0无取代-20.01436932.347074-2.2571531.342990.04913-0.33067126.832572-0.328260.390-0.3050.427-0.369
1p-F-20.10887232.188125-4.0405371.342630.03662-0.32980128.984053-0.337840.388-0.3030.411-0.367
2p-COH-21.07963032.784260-1.9145331.343760.05125-0.31824127.039399-0.325570.388-0.3060.421-0.371
3p-COMe-21.18818932.564631-1.8655871.343550.05019-0.31977127.014927-0.326150.389-0.3060.422-0.370
4p-CN-21.70965029.184235-2.9568271.336480.00701-0.34663126.735685-0.351930.408-0.2900.454-0.356
5p-CF3-21.39589530.172563-2.8997241.338690.02973-0.35163126.827929-0.346720.403-0.2950.447-0.360
6p-NO2-22.73123629.058106-2.3098641.33497-0.03099-0.36007126.887542-0.356080.417-0.2870.459-0.354
7p-Me-20.84117633.337096-2.0268571.344020.05022-0.31879126.958451-0.326720.384-0.3060.419-0.371
8p-iPr-21.79248133.990334-1.0962601.343800.05007-0.31884127.006769-0.326250.385-0.3060.420-0.371
9p-OH-20.74391234.205570-2.9210591.345300.04600-0.30347126.728782-0.311100.380-0.3090.393-0.373

Last rows

function groups/parametersBinding EnergyActivation EnergyHeat of ReactionC-F Bond LengthLUMO of Equilibrium StructureHOMO of Equilibrium StructureC-F Bond Energyb-SOMOC1-MullikenF-MullikenC1-NBOF-NBO
40o-OMe-20.11326431.019074-7.3249241.339870.05675-0.30501125.997105-0.307490.343000-0.2920000.38700-0.36100
41o-NH2-20.44898230.933105-5.0991461.352830.06011-0.28796126.836714-0.288690.338000-0.3150000.37600-0.38200
42o-NMe2-20.69747631.349145-4.8399851.348120.05894-0.28360123.117462-0.273850.350000-0.3000000.40200-0.37200
43o-CH2OH-20.40505626.893196-5.2215111.349650.05562-0.32179127.022457-0.324800.348000-0.3070000.42600-0.37600
44o-CH2OMe-20.45965028.522212-5.9669931.349510.05440-0.32268126.964098-0.325790.349000-0.3060000.42700-0.37400
45o-n-21.46419930.3717041.8067461.334550.02777-0.34622126.289613-0.304370.446028-0.2521140.63947-0.36186
46nap-1-23.20621132.735779-0.9851091.344210.01493-0.29629126.726554-0.299680.308166-0.2625440.45457-0.36840
47nap-2-23.01252434.2253050.0221641.342600.01346-0.29897126.794608-0.298630.321520-0.2585420.43112-0.36825
48nap-N-1-23.27609032.007691-1.7030501.34333-0.00036-0.31347126.257798-0.319300.308508-0.2613810.45491-0.36565
49nap-N-2-23.59553134.1847370.1759101.34126-0.00084-0.31741126.745926-0.318540.321742-0.2520910.43336-0.36539